



import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


a      :  Optional[int]			     =			'bart'
a      :  Tuple			     =			True







@st.cache(allow_output_mutation=lowerCAmelCase__  )
def  lowerCAmelCase_	():





			"""simple docstring"""


			if LOAD_DENSE_INDEX:
						UpperCAmelCase_:		Tuple							      =     AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""  )
						UpperCAmelCase_:		Tuple							      =     AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""  ).to("""cuda:0"""  )
						UpperCAmelCase_:		Optional[Any]							      =     qar_model.eval()
			else:
						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     (None, None)
			if MODEL_TYPE == "bart":
						UpperCAmelCase_:		Optional[int]							      =     AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""  )
						UpperCAmelCase_:		List[str]							      =     AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""  ).to("""cuda:0"""  )
						UpperCAmelCase_:		Any							      =     torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth"""  )
						sas_model.load_state_dict(save_dict["""model"""]  )
						UpperCAmelCase_:		List[Any]							      =     sas_model.eval()
			else:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     make_qa_sas_model(
						    model_name="""t5-small"""		,						from_file="""seq2seq_models/eli5_t5_model_1024_4.pth"""		,						device="""cuda:0"""  )
			return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)







@st.cache(allow_output_mutation=lowerCAmelCase__  )
def  lowerCAmelCase_	():





			"""simple docstring"""


			if LOAD_DENSE_INDEX:
						UpperCAmelCase_:		List[Any]							      =     faiss.StandardGpuResources()
						UpperCAmelCase_:		str							      =     datasets.load_dataset(path="""wiki_snippets"""		,						name="""wiki40b_en_100_0"""  )["""train"""]
						UpperCAmelCase_:		Union[str, Any]							      =     np.memmap(
						    """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""		,						dtype="""float32"""		,						mode="""r"""		,						shape=(wikiaab_passages.num_rows, 1_2_8)		,						)
						UpperCAmelCase_:		Tuple							      =     faiss.IndexFlatIP(1_2_8  )
						UpperCAmelCase_:		List[Any]							      =     faiss.index_cpu_to_gpu(lowerCAmelCase__		,						1		,						lowerCAmelCase__  )
						wikiaab_gpu_index_flat.add(lowerCAmelCase__  )  # TODO fix for larger GPU
			else:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     (None, None)
			UpperCAmelCase_:		Any							      =     Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]  )
			return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)







@st.cache(allow_output_mutation=lowerCAmelCase__  )
def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Tuple							      =     datasets.load_dataset("""eli5"""		,						name="""LFQA_reddit"""  )
			UpperCAmelCase_:		Tuple							      =     elia["""train_eli5"""]
			UpperCAmelCase_:		Optional[Any]							      =     np.memmap(
			    """eli5_questions_reps.dat"""		,						dtype="""float32"""		,						mode="""r"""		,						shape=(elia_train.num_rows, 1_2_8)  )
			UpperCAmelCase_:		List[Any]							      =     faiss.IndexFlatIP(1_2_8  )
			eli5_train_q_index.add(lowerCAmelCase__  )
			return (elia_train, eli5_train_q_index)


a     ,a     ,a      :  Union[str, Any]			     =			load_indexes()
a     ,a     ,a     ,a      :  Optional[int]			     =			load_models()
a     ,a      :  int			     =			load_train_data()







def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		Optional[int]=1_0  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     embed_questions_for_retrieval([question]		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     eli5_train_q_index.search(lowerCAmelCase__		,						lowerCAmelCase__  )
			UpperCAmelCase_:		str							      =     [elia_train[int(lowerCAmelCase__  )] for i in I[0]]
			return nn_examples







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		Union[str, Any]="wiki40b"		,						lowerCAmelCase__:		Tuple="dense"		,						lowerCAmelCase__:		List[Any]=1_0  ):





			"""simple docstring"""


			if source == "none":
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     (""" <P> """.join(["""""" for _ in range(1_1  )]  ).strip(), [])
			else:
						if method == "dense":
									UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     query_qa_dense_index(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
						else:
									UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     query_es_index(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						index_name="""english_wiki40b_snippets_100w"""		,						n_results=lowerCAmelCase__		,						)
			UpperCAmelCase_:		List[str]							      =     [
			    (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
			]
			UpperCAmelCase_:		Dict							      =     """question: {} context: {}""".format(lowerCAmelCase__		,						lowerCAmelCase__  )
			return question_doc, support_list







@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowerCAmelCase__  : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCAmelCase__  : None),
    }  )
def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		Dict=6_4		,						lowerCAmelCase__:		List[str]=2_5_6		,						lowerCAmelCase__:		Optional[Any]=False		,						lowerCAmelCase__:		Tuple=2		,						lowerCAmelCase__:		Any=0.95		,						lowerCAmelCase__:		Dict=0.8  ):





			"""simple docstring"""


			with torch.no_grad():
						UpperCAmelCase_:		Optional[int]							      =     qa_sas_generate(
						    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						num_answers=1		,						num_beams=lowerCAmelCase__		,						min_len=lowerCAmelCase__		,						max_len=lowerCAmelCase__		,						do_sample=lowerCAmelCase__		,						temp=lowerCAmelCase__		,						top_p=lowerCAmelCase__		,						top_k=lowerCAmelCase__		,						max_input_length=1_0_2_4		,						device="""cuda:0"""		,						)[0]
			return (answer, support_list)


st.title('Long Form Question Answering with ELI5')

# Start sidebar
a      :  Tuple			     =			'<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
a      :  Any			     =			'\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class="img-container"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
a      :  int			     =			'\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)

a      :  List[str]			     =			[
    'Answer the question',
    'View the retrieved document only',
    'View the most similar ELI5 question and answer',
    'Show me everything, please!',
]
a      :  Optional[Any]			     =			st.sidebar.checkbox('Demo options')
if demo_options:
	a      :  Dict			     =			st.sidebar.selectbox(
	    '',
	    action_list,
	    index=3,
	)
	a      :  int			     =			action_list.index(action_st)
	a      :  Optional[int]			     =			st.sidebar.selectbox(
	    '',
	    ['Show full text of passages', 'Show passage section titles'],
	    index=0,
	)
	a      :  Optional[Any]			     =			show_type == 'Show full text of passages'
else:
	a      :  Tuple			     =			3
	a      :  Dict			     =			True

a      :  Union[str, Any]			     =			st.sidebar.checkbox('Retrieval options')
if retrieval_options:
	a      :  Optional[Any]			     =			'\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    '
	st.sidebar.markdown(retriever_info)
	a      :  Tuple			     =			st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
	a      :  List[Any]			     =			st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
	a      :  int			     =			'wiki40b'
	a      :  List[Any]			     =			'dense'

a      :  str			     =			'beam'
a      :  Tuple			     =			2
a      :  Dict			     =			64
a      :  Tuple			     =			256
a      :  Optional[Any]			     =			None
a      :  Tuple			     =			None
a      :  Union[str, Any]			     =			st.sidebar.checkbox('Generation options')
if generate_options:
	a      :  Tuple			     =			'\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder\'s output probabilities.\n    '
	st.sidebar.markdown(generate_info)
	a      :  Optional[Any]			     =			st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
	a      :  int			     =			st.sidebar.slider(
	    'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
	)
	a      :  str			     =			st.sidebar.slider(
	    'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
	)
	if sampled == "beam":
		a      :  Optional[Any]			     =			st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
	else:
		a      :  List[Any]			     =			st.sidebar.slider(
		    'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
		)
		a      :  Optional[int]			     =			st.sidebar.slider(
		    'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
		)
		a      :  Optional[Any]			     =			None

# start main text
a      :  Union[str, Any]			     =			[
    '<MY QUESTION>',
    'How do people make chocolate?',
    'Why do we get a fever when we are sick?',
    'How can different animals perceive different colors?',
    'What is natural language processing?',
    'What\'s the best way to treat a sunburn?',
    'What exactly are vitamins ?',
    'How does nuclear energy provide electricity?',
    'What\'s the difference between viruses and bacteria?',
    'Why are flutes classified as woodwinds when most of them are made out of metal ?',
    'Why do people like drinking coffee even though it tastes so bad?',
    'What happens when wine ages? How does it make the wine taste better?',
    'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
    'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
    'How does New Zealand have so many large bird predators?',
]
a      :  Tuple			     =			st.selectbox(
    'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
	a      :  Any			     =			st.text_input('Enter your question here:', '')
else:
	a      :  List[str]			     =			question_s

if st.button('Show me!'):
	if action in [0, 1, 3]:
		if index_type == "mixed":
			a     ,a      :  Any			     =			make_support(question, source=wiki_source, method='dense', n_results=10)
			a     ,a      :  Any			     =			make_support(question, source=wiki_source, method='sparse', n_results=10)
			a      :  Optional[int]			     =			[]
			for res_d, res_s in zip(support_list_dense, support_list_sparse):
				if tuple(res_d) not in support_list:
					support_list += [tuple(res_d)]
				if tuple(res_s) not in support_list:
					support_list += [tuple(res_s)]
			a      :  List[str]			     =			support_list[:10]
			a      :  List[Any]			     =			'<P> ' + ' <P> '.join([res[-1] for res in support_list])
		else:
			a     ,a      :  int			     =			make_support(question, source=wiki_source, method=index_type, n_results=10)
	if action in [0, 3]:
		a     ,a      :  Union[str, Any]			     =			answer_question(
		    question_doc,
		    sas_model,
		    sas_tokenizer,
		    min_len=min_len,
		    max_len=int(max_len),
		    sampling=(sampled == 'sampled'),
		    n_beams=n_beams,
		    top_p=top_p,
		    temp=temp,
		)
		st.markdown('### The model generated answer is:')
		st.write(answer)
	if action in [0, 1, 3] and wiki_source != "none":
		st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
		for i, res in enumerate(support_list):
			a      :  Any			     =			'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
			a      :  Dict			     =			res[1].strip()
			if sec_titles == "":
				a      :  Union[str, Any]			     =			'[{}]({})'.format(res[0], wiki_url)
			else:
				a      :  Union[str, Any]			     =			sec_titles.split(' & ')
				a      :  str			     =			' & '.join(
				    ['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
				)
			st.markdown(
			    '{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'.format(i + 1, res[0], sections),
			    unsafe_allow_html=True,
			)
			if show_passages:
				st.write(
				    '> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
				)
	if action in [2, 3]:
		a      :  Union[str, Any]			     =			find_nearest_training(question)
		a      :  str			     =			nn_train_list[0]
		st.markdown(
		    '--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
		)
		a      :  Any			     =			[
		    '{}. {}'.format(i + 1, '  \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
		    for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
		    if i == 0 or sc > 2
		]
		st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))


a      :  Any			     =			'\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'

st.sidebar.markdown(disclaimer, unsafe_allow_html=True)











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a      :  int			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     """huggingface/label-files"""
			UpperCAmelCase_:		Optional[int]							      =     """imagenet-1k-id2label.json"""
			UpperCAmelCase_:		Union[str, Any]							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Tuple							      =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase_:		int							      =     """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_:		Any							      =     BitConfig(
			    conv_layer=lowerCAmelCase__		,						num_labels=1_0_0_0		,						idalabel=lowerCAmelCase__		,						labelaid=lowerCAmelCase__		,						)

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			if "stem.conv" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""stem.conv"""		,						"""bit.embedder.convolution"""  )
			if "blocks" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""blocks"""		,						"""layers"""  )
			if "head.fc" in name:
						UpperCAmelCase_:		int							      =     name.replace("""head.fc"""		,						"""classifier.1"""  )
			if name.startswith("""norm"""  ):
						UpperCAmelCase_:		List[Any]							      =     """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_:		str							      =     """bit.encoder.""" + name

			return name







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Optional[int]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		str=False  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_config(lowerCAmelCase__  )

			# load original model from timm
			UpperCAmelCase_:		str							      =     create_model(lowerCAmelCase__		,						pretrained=lowerCAmelCase__  )
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_:		Optional[Any]							      =     timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_:		Any							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     val.squeeze() if """head""" in key else val

			# load HuggingFace model
			UpperCAmelCase_:		Union[str, Any]							      =     BitForImageClassification(lowerCAmelCase__  )
			model.eval()
			model.load_state_dict(lowerCAmelCase__  )

			# create image processor
			UpperCAmelCase_:		Any							      =     create_transform(**resolve_data_config({}		,						model=lowerCAmelCase__  )  )
			UpperCAmelCase_:		List[Any]							      =     transform.transforms

			UpperCAmelCase_:		Tuple							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_:		int							      =     BitImageProcessor(
			    do_resize=lowerCAmelCase__		,						size={"""shortest_edge""": timm_transforms[0].size}		,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,						do_center_crop=lowerCAmelCase__		,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,						do_normalize=lowerCAmelCase__		,						image_mean=timm_transforms[-1].mean.tolist()		,						image_std=timm_transforms[-1].std.tolist()		,						)

			UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
			UpperCAmelCase_:		Tuple							      =     transform(lowerCAmelCase__  ).unsqueeze(0  )
			UpperCAmelCase_:		Dict							      =     processor(lowerCAmelCase__		,						return_tensors="""pt"""  ).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__		,						lowerCAmelCase__  )

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_:		Dict							      =     model(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     outputs.logits

			print("""Logits:"""		,						logits[0, :3]  )
			print("""Predicted class:"""		,						model.config.idalabel[logits.argmax(-1  ).item()]  )
			UpperCAmelCase_:		List[str]							      =     timm_model(lowerCAmelCase__  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__		,						outputs.logits		,						atol=1e-3  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print(F'Pushing model {model_name} and processor to the hub'  )
						model.push_to_hub(F'ybelkada/{model_name}'  )
						processor.push_to_hub(F'ybelkada/{model_name}'  )


if __name__ == "__main__":
	a      :  Dict			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='resnetv2_50x1_bitm',
	    type=str,
	    help='Name of the BiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model to the hub.',
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






